About Atari

Atari is an interactive entertainment company and an iconic gaming industry brand recognized worldwide for its multi-platform games and licensed products. Atari owns and/or manages a portfolio of more than 400 games and franchises, including globally recognized brands such as Asteroids®, Centipede®, Missile Command®, Pong®, and RollerCoaster Tycoon®.

The Atari family of brands includes Digital Eclipse, Nightdive Studios, Infogrames, AtariAge, MobyGames, as well as Coatsink, Early Morning Studios, and Stormteller Games—spanning game development, publishing, and community experiences worldwide.

Atari operates internationally with offices in New York, Paris, Germany, and India.

Overview

Architect, build, and own AI systems that automate expert-intensive technical workflows end-to-end — from CLI frameworks, MCP servers, and agent tooling through to production deployment, business outcome tracking, and continuous improvement. You solve real business problems with AI, ensure solutions are fully implemented and adopted, and measure whether they are actually working.

What You’ll Do

System Architecture and Framework Design

• Own the end-to-end architecture of AI automation systems: workflow decomposition, component communication, human checkpoint placement, and failure behaviour.
• Design and build the internal CLI frameworks, reusable libraries, and agent scaffolding that all pipelines are built on.
• Author and maintain agent instruction files (SKILL.md, CLAUDE.md, system prompts), context configurations, and MCP server definitions that govern agent behaviour and tool access.
• Configure and govern AI coding environments such as Claude Code and Codex CLI: wiring MCP servers, defining tool permissions, building slash commands, and setting the engineering standards the team operates by.
• Evaluate and document architectural trade-offs across reliability, latency, cost, and maintainability.

Pipeline Development and Integration

• Build production-grade AI pipelines in Python: orchestration logic, tool definitions, structured prompting, context assembly, output schema validation, and retry and escalation strategies.
• Integrate AI systems with external tooling — version control, build pipelines, platform SDKs, compliance databases, internal APIs — with well-defined permission boundaries and failure handling.
• Design context assembly: how domain knowledge, runtime state, retrieved documents, and tool outputs compose into the precise input each pipeline stage needs.
• Build and operate multi-agent systems: orchestrator-worker patterns, agent memory management, structured handoffs, and conflict resolution between concurrent agents.

Prompt Engineering

• Design, write, and version all system prompts and agent instructions as first-class engineering artefacts: role definition, behavioural constraints, output format contracts, tool usage rules, and fallback handling — with chain-of-thought, few-shot, and structured output techniques applied deliberately per task.
• Own output schema design and prompt regression testing: define format contracts downstream stages depend on, validate against them, and maintain a ground-truth eval set that gates every instruction or model change before deployment.

Context Engineering, Knowledge, and Domain Collaboration

• Engineer context windows with precision: determine what enters each pipeline stage, in what order, and at what granularity — balancing model comprehension and task accuracy against token cost and latency through compression, summarisation, and selective retrieval where needed.
• Work directly with domain experts to extract specialist knowledge and translate it into agent behaviour: decision logic, edge case handling, quality criteria, and failure modes.
• Partner with the RAG Engineer to define retrieval requirements — what knowledge is needed, under what conditions, at what granularity — ensuring retrieved context is precise and actionable.
• Build and maintain structured runtime knowledge assets: curated document corpora, rule sets, decision trees, and validation reference libraries.

Evaluation, Quality, and Reliability

• Build and own the evaluation framework: test suites, regression benchmarks, LLM-as-judge pipelines, per-stage quality metrics, and end-to-end pass rates against expert-validated ground truth.
• Implement production monitoring across all pipelines: latency, token consumption, per-stage success rates, failure distribution, and output quality drift, using tooling such as LangFuse, Arize, or equivalent.
• Run structured failure analysis: diagnose root causes in context assembly, orchestration logic, knowledge coverage, or tool integration — and implement targeted fixes.
• Define and track automation rate as a first-class metric: workflow instances completing end-to-end without human intervention and the verified quality of those outputs.
• Measure and report on business effectiveness of deployed systems: whether the solution is achieving its intended objective, where gaps remain, and what changes would close them.

Governance, Safety, and Technical Leadership

• Implement full audit trails: inputs received, tools called, outputs produced, and human review triggers — making system behaviour traceable and defensible.
• Enforce versioning of all agent instructions, context configurations, and system prompts as engineered artefacts subject to regression testing and controlled rollout.
• Set the technical standard for AI system development within the organisation — architecture patterns, evaluation practices, framework conventions, and quality gates — documented so what you build is maintainable by others.
• Collaborate with engineering, domain, and product teams throughout implementation: answering technical questions, unblocking dependencies, and ensuring the system is correctly adopted by everyone depending on it.
• Engage engineering leadership on roadmap priorities, scope trade-offs, and technical risk with clear technical perspective.

Requirements & Qualifications

• Proven track record of building production AI automation systems from scratch — independently scoping, architecting, and delivering end-to-end from framework design through deployment and ongoing improvement.
• Hands-on expertise with AI coding agents — Claude Code, Codex CLI, Cursor, or equivalent — including MCP server configuration, tool permission design, agent instruction authoring, and CLI-driven workflow automation.
• Experience designing, building, and deploying MCP servers and custom tools from scratch: tool schema design, authentication, permission boundaries, and governing what agents can access and act on.
• Experience creating internal tooling and automation that measurably improved engineering team efficiency — reducing manual processes, accelerating workflows, and enabling teams to operate at higher leverage.
• Experience defining and tracking productivity and efficiency metrics for AI systems: automation rate, time-to-completion reduction, human intervention rate, and business outcome measurement.
• Experience working with data scientists and domain experts to implement AI solutions that measurably improved team productivity and workflow efficiency.
• Deep prompt engineering and context engineering practice: system prompt authoring, few-shot design, chain-of-thought and structured output techniques, token budget management, context window strategy, and prompt versioning with regression testing.
• Proficiency with LLM orchestration frameworks — LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent — with the judgment to know when to use them and when to build leaner.
• Experience building internal CLI frameworks, agent scaffolding, and reusable libraries that others build on.
• Production Python engineering: modular, testable, well-logged code with proper error handling.
• Experience integrating AI systems with external tools and APIs: tool definition design, permission management, rate limit and failure handling.
• Experience building AI evaluation frameworks: test suites, regression benchmarks, LLM-as-judge pipelines, and production quality monitoring.
• Cloud platform experience (AWS, Azure, or GCP): deploying and monitoring AI workloads with containerisation and orchestration tooling.

Preferred / Nice-to-Have

• Experience in the gaming industry: game development pipelines, engine architectures such as Unity or Unreal, platform certification processes, or cross-platform porting workflows.
• Familiarity with game engine scripting, asset pipelines, or platform-specific SDK integration (Xbox GDK, PlayStation SDK, or similar).

To Apply

Please submit your resume and a brief cover letter outlining your experience and interest in the role. If available, you are also welcome to include a link to your portfolio.

EEO Statement

Atari is an equal opportunity employer and we are committed to providing a workplace free from harassment and discrimination.  We are committed to equal employment regardless of race, religion or lack thereof, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, medical condition, veteran status, ancestry, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

About Atari

Atari is an interactive entertainment company and an iconic gaming industry brand recognized worldwide for its multi-platform games and licensed products. Atari owns and/or manages a portfolio of more than 400 games and franchises, including globally recognized brands such as Asteroids®, Centipede®, Missile Command®, Pong®, and RollerCoaster Tycoon®.

The Atari family of brands includes Digital Eclipse, Nightdive Studios, Infogrames, AtariAge, MobyGames, as well as Coatsink, Early Morning Studios, and Stormteller Games—spanning game development, publishing, and community experiences worldwide.

Atari operates internationally with offices in New York, Paris, Germany, and India.

Overview

The Technical Game Architect is the domain authority on game engine architecture and cross-platform porting - bringing deep, hands-on expertise to evaluate technical outputs, identify gaps, and ensure every port meets the standard required for platform certification and release. This role drives technical excellence across the porting process, working at the intersection of game development expertise and tooling and automation to make cross-platform delivery faster, more reliable, and more efficient.

What You’ll Do

Technical Review and Validation

• Review code changes, configurations, and platform integrations for technical correctness - catching errors, incorrect API usage, missing edge cases, and certification-blocking patterns.
• Evaluate technical outputs against deep knowledge of PC and console game behaviour - identifying structural failures, not just compilation errors.
• Make gate decisions at every major milestone - assessing technical soundness and platform readiness with the authority to stop a port that is not ready.

Domain Expertise and Knowledge Contribution

• Serve as the primary source of porting knowledge - translating hands-on experience into structured patterns, decision rules, error-fix pairs, and platform-specific guidance.
• Diagnose knowledge and process gaps when outputs are consistently incorrect - identify the root cause and provide the expertise to close it.
• Keep domain knowledge current across engine versions, platform SDKs, and certification requirements.
• Document porting patterns, platform behaviours, and certification requirements precisely and unambiguously.

Engineering Team Collaboration

• Work as a continuous partner to the engineering team throughout every port - embedded in the process from scoping through launch, not a downstream reviewer.
• Identify what domain knowledge is needed, in what form, and at what precision - ensuring it is correctly structured and usable before it informs any tooling or automation.
• Jointly diagnose tooling failures with the engineering team - bringing domain expertise to identify what is wrong while the team diagnoses why, closing gaps together.
• Contribute domain expertise to AI-assisted automation tooling - reviewing outputs, identifying systematic errors, and providing structured knowledge that improves accuracy over time.
• Validate engineering fixes - confirming correct technical output before the next port begins.

Platform and Hardware Validation

• Test ports on physical console hardware: verify rendering, input, platform services, suspend and resume, save persistence, and device-specific requirements.
• Validate certification compliance - distinguishing blocking failures from warnings and providing precise guidance on remediation.
• Provide authoritative judgment on controller input and platform-native behaviour.

End-to-End Porting Lifecycle Ownership

• Lead viability assessment: evaluate engine version, codebase structure, dependencies, and platform complexity to determine effort, risk, and feasibility.
• Define the technical porting plan: work sequencing, dependency mapping, risk identification, and system-by-system approach.
• Own the first build milestone: get the game compiling and running on the target platform, resolving the initial failures that block all subsequent work.
• Drive performance and optimisation: identify where the game exceeds console CPU, GPU, and memory constraints and define the approach to hit stable targets.
• Manage certification preparation, submission, and response to platform holder feedback through to approval.
• Communicate with game producers and stakeholders throughout the port - identifying requirements, setting expectations, surfacing what will be handled through tooling and automation, and flagging where decisions or input are needed.
• Coordinate with game producers on launch readiness: release timing, store metadata, platform requirements, and post-launch patch planning.

Requirements & Qualifications

• 10+ years of hands-on game development experience, with a proven track record of shipping titles across PC and at least one major console platform (Xbox, PlayStation, or Nintendo Switch).
• Deep expertise in Unity or Unreal Engine: architecture, build pipelines, rendering, input, asset management, and platform-specific configuration.
• Extensive console porting experience across multiple titles and SDK generations - full lifecycle from first build through certification submission.
• Deep working knowledge of at least one console SDK (GDK/GDKX, PlayStation SDK, or Nintendo SDK): platform services, certification requirements, and hardware constraints.
• Strong understanding of where PC and console architectures diverge and where those differences create porting complexity or incompatibility.
• Experience reviewing code for platform correctness - identifying incorrect API usage, missing behaviours, performance issues, and certification failures on sight.
• Ability to rapidly learn and apply new engines, platform SDKs, and tooling - transferring structural porting knowledge to unfamiliar environments independently.
• Clear, precise communication of technical judgments to both engineering teams and non-technical stakeholders.
• Experience with physical console devkit testing: deploying builds, capturing logs, diagnosing failures, and validating platform services on device.

Preferred / Nice-to-Have

• Experience across multiple engine and platform combinations - Unity and Unreal, or Xbox and PlayStation, or console and PC simultaneously.
• Familiarity with tooling and automation in game development workflows - reviewing tool-generated outputs or working alongside automated systems in a technical oversight capacity.
• Experience authoring technical documentation, porting guides, or certification reference materials.

To Apply

Please submit your resume and a brief cover letter outlining your experience and interest in the role. If available, you are also welcome to include a link to your portfolio.

EEO Statement

Atari is an equal opportunity employer and we are committed to providing a workplace free from harassment and discrimination.  We are committed to equal employment regardless of race, religion or lack thereof, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, medical condition, veteran status, ancestry, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

About Atari

Atari is an interactive entertainment company and an iconic gaming industry brand recognized worldwide for its multi-platform games and licensed products. Atari owns and/or manages a portfolio of more than 400 games and franchises, including globally recognized brands such as Asteroids®, Centipede®, Missile Command®, Pong®, and RollerCoaster Tycoon®.

The Atari family of brands includes Digital Eclipse, Nightdive Studios, Infogrames, AtariAge, MobyGames, as well as Coatsink, Early Morning Studios, and Stormteller Games—spanning game development, publishing, and community experiences worldwide.

Atari operates internationally with offices in New York, Paris, Germany, and India.

Overview

Build and own the data pipelines and knowledge systems that power AI automation. You own the schema, the sources, the quality, and the completeness — working closely with the engineering team to ensure the right knowledge is always in the system and working.

What You’ll Do

Data Schema Design and Ownership

• Design and own canonical data schemas that structure knowledge for AI consumption — versioned, documented, and treated as engineering artefacts.
• Evolve schemas as AI system requirements change: assess downstream impact and coordinate with the engineering team before any rollout.
• Keep the knowledge model legible to domain experts, AI engineers, and future team members through clear documentation of schema decisions.

Source Integration and Pipeline Engineering

• Identify and connect to source systems — databases, repositories, APIs, workflow outputs, domain expert inputs — and build reliable, monitored ingestion pipelines for each.
• Build and maintain production-grade data pipelines using pipeline orchestration tooling (such as Apache Airflow, Prefect, or equivalent): extraction, transformation, validation, loading, error handling, and full observability.
• Keep pipelines current as sources change, new knowledge is produced, or schemas evolve — downstream AI systems are never fed stale or broken data.

Data Cleaning, Transformation, and Quality

• Transform raw source material into structured, consistent, agent-consumable knowledge: normalise formats, resolve conflicts, enforce schema conformance, and eliminate ambiguity.
• Own data quality end-to-end: define standards, build validation logic at ingestion using data quality frameworks (such as Great Expectations or equivalent), and maintain quality metrics across the full knowledge estate.
• Trace quality issues to their root — pipeline fault, schema gap, or source problem — and fix there, not downstream.

Data Systems Maintenance and Lifecycle Management

• Maintain all data pipelines and knowledge systems in production: monitor health, manage dependencies, handle source system changes, and ensure nothing degrades silently over time.
• Run periodic data cleanup: identify and remove stale, duplicate, or degraded knowledge assets that would dilute AI system quality if left in place.
• Manage knowledge deprecation cleanly: when data becomes outdated or superseded, retire it from the system without breaking downstream pipelines or AI system behaviour.

AI Knowledge System Partnership

• Work in a closed loop with the engineering team: diagnose knowledge gaps when AI outputs fail, fix at the pipeline or schema level, and validate through the evaluation framework.
• Understand how RAG retrieval and context assembly work well enough to make data design decisions with AI consumption as the primary constraint.
• Proactively surface knowledge gaps by monitoring AI system failures and escalations, and address root causes before they become production issues.
• Extract specialist knowledge from domain experts and convert it into structured, pipeline-managed data assets the AI system can rely on.

Data Infrastructure and Governance

• Select and manage data storage infrastructure appropriate to each knowledge type — relational databases, document stores, and vector databases (such as Pinecone, Weaviate, or pgvector) — with AI consumption as the deciding factor.
• Implement full data lineage and audit trails: origin, transformation history, and change log for every knowledge asset.
• Enforce data versioning and change management across the knowledge estate, with rollback capability preserved at every step.
• Build a data estate that is legible and accessible across the organisation — laying the foundation for intelligence and analytics as the product portfolio grows.

Stakeholder Communication and Collaboration

• Translate between domain and engineering language in both directions — extracting knowledge requirements from non-technical stakeholders and communicating data decisions and tradeoffs back clearly.
• Work with domain experts to understand how data should be structured and what outputs need to look like — then convert that directly into schema and pipeline requirements.
• Keep engineering, product, and domain stakeholders appropriately informed on pipeline health, data quality, and knowledge gaps — right level of detail, right audience.

Requirements & Qualifications

• Proven track record building and owning production data pipelines end-to-end: source integration, transformation, validation, loading, observability, and ongoing maintenance.
• Strong data schema design skills: canonical data modelling, schema evolution management, downstream impact assessment, and decision documentation.
• Experience integrating with diverse source systems — APIs, databases, document repositories, event streams — with reliable, monitored ingestion pipelines.
• Hands-on experience with pipeline orchestration tooling (such as Apache Airflow, Prefect, or equivalent): scheduling, dependency management, failure handling, and pipeline observability.
• Data cleaning and transformation expertise: normalisation, conflict resolution, format standardisation, and quality validation at scale using data transformation tools (such as dbt or equivalent).
• Experience with vector databases (such as Pinecone, Weaviate, or pgvector) and relational or document stores: schema design, indexing, and production data management.
• Working knowledge of how RAG systems and context assembly operate — sufficient to make data design decisions with AI consumption as the primary constraint.
• Experience partnering with AI or ML engineers: translating knowledge requirements into pipeline tasks and closing feedback loops between AI output quality and data quality.
• Ability to interrogate and profile data across sources — querying, inspecting, and validating content using SQL and Python to identify anomalies, gaps, and quality issues before they reach AI systems.
• Cloud platform experience (AWS, Azure, or GCP): deploying and operating data pipelines and storage systems at production reliability standards.

Preferred / Nice-to-Have

• Experience in the gaming industry: game development pipelines, content management, or platform-specific data and metadata requirements.
• Familiarity with game engine data formats, asset pipelines, or platform SDK data structures (Xbox GDK, PlayStation SDK, or similar).

To Apply

Please submit your resume and a brief cover letter outlining your experience and interest in the role. If available, you are also welcome to include a link to your portfolio.

EEO Statement

Atari is an equal opportunity employer and we are committed to providing a workplace free from harassment and discrimination.  We are committed to equal employment regardless of race, religion or lack thereof, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, medical condition, veteran status, ancestry, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

About Us: Founded in 1972, Atari is one of the world’s most iconic consumer brands and a pioneer in the video game industry, known for creating classics like Pong, Asteroids, and Centipede. Today, Atari Inc. continues to build on its legacy by developing games, hardware, and experiences that honor the past while driving innovation for the future. 

Over the past two years, we've been building Atari India, a growing team that plays a critical role in supporting our global operations. We're proud of the team we've assembled so far, and we’re just getting started. As part of a lean, high-impact organization, the team in India works closely with colleagues in North America and Europe on projects that move the company forward. Whether you're helping launch a new game, keeping our infrastructure secure, or supporting day-to-day operations, your work here matters. Join us as we continue to grow Atari India and build the future of a legendary brand.

Position: Principal AI Systems Engineer 
Experience: 8+ Years
Location: Netaji Subhash Place, Pitampura, New Delhi
Employment Type: Full-Time (Hybrid)
Reports to: Senior Director of Technology, India

About the Role 

Architect, build, and own AI systems that automate expert-intensive technical workflows end-to end — from CLI frameworks, MCP servers, and agent tooling through to production deployment, business outcome tracking, and continuous improvement. You solve real business problems with AI, ensure solutions are fully implemented and adopted, and measure whether they are actually working.

Responsibilities

System Architecture

• Own end-to-end architecture of AI automation systems: workflow decomposition, component communication, human checkpoints, and failure behaviour
• Design and build internal CLI frameworks, reusable libraries, and agent scaffolding
• Author and maintain agent instruction files (SKILL.md, CLAUDE.md, system prompts) and MCP server definitions
• Configure Claude Code and Codex CLI environments: MCP wiring, tool permissions, slash commands, and engineering standards
• Evaluate and document architectural trade-offs across reliability, latency, cost, and maintainability

Pipeline Development

• Build production-grade AI pipelines in Python: orchestration, structured prompting, context assembly, schema validation, and retry strategies
• Integrate AI systems with external tooling — version control, build pipelines, SDKs, compliance databases, internal APIs
• Design context assembly: how domain knowledge, runtime state, retrieved documents, and tool outputs compose into the precise input each pipeline stage needs
• Build and operate multi-agent systems: orchestrator-worker patterns, agent memory, structured handoffs, and conflict resolution

Prompt & Context Engineering

• Design, version, and maintain system prompts and agent instructions as first-class engineering artefacts
• Own output schema design and prompt regression testing with a maintained ground-truth eval set
• Engineer context windows with precision — balancing accuracy, token cost, and latency through compression and selective retrieval
• Partner with the RAG Engineer to define retrieval requirements — what knowledge is needed, under what conditions, and at what granularity
• Build and maintain structured runtime knowledge assets: curated document corpora, rule sets, decision trees, and validation reference libraries
• Work with domain experts to translate specialist knowledge into agent behaviour: decision logic, edge cases, and failure modes

Evaluation & Reliability

• Build and own the evaluation framework: test suites, regression benchmarks, LLM-as-judge pipelines, and per-stage quality metrics
• Implement production monitoring using LangFuse, Arize, or equivalent — latency, token usage, success rates, and output quality drift
• Run structured failure analysis and implement targeted fixes across context assembly, orchestration, and tool integration
• Define automation rate as a first-class metric and report on business effectiveness of deployed systems

Governance & Technical Leadership

• Implement full audit trails — inputs, tools called, outputs, and human review triggers
• Enforce versioning of all agent instructions and system prompts as engineered artefacts with controlled rollout
• Set the technical standard for AI development across the organization — architecture patterns, eval practices, and quality gates
• Collaborate with engineering, product, and domain teams; engage leadership on roadmap priorities and technical risk
  
  

Requirements

• Proven track record of building production AI automation systems from scratch — end-to-end from architecture through deployment
• Hands-on expertise with Claude Code, Codex CLI, Cursor, or equivalent — including MCP server configuration and agent instruction authoring
• Experience designing and deploying MCP servers and custom tools: tool schema, authentication, and permission boundaries
• Experience building internal CLI frameworks, agent scaffolding, and reusable libraries that others build on
• Experience creating internal tooling and automation that measurably improved engineering team efficiency — reducing manual processes and accelerating workflows
• Experience working with data scientists and domain experts to implement AI solutions that measurably improved team productivity
• Deep prompt and context engineering: system prompts, few-shot design, chain-of-thought, token budget management, and prompt versioning
• Proficiency with LLM orchestration frameworks — LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent
• Experience building AI evaluation frameworks: test suites, regression benchmarks, LLM-as-judge, and production quality monitoring
• Production Python engineering: modular, testable, well-logged code with proper error handling
• Cloud platform experience (AWS, Azure, or GCP): deploying and monitoring AI workloads with containerisation
• Experience integrating AI systems with external APIs — tool definition, permission management, and failure handling
• Experience defining and tracking AI productivity metrics: automation rate, time-to-completion, and human intervention rate
  
  

Bonus Points

• Experience in gaming: game development pipelines, Unity/Unreal engine architectures, or platform certification processes
• Familiarity with game engine scripting, asset pipelines, or platform SDKs (Xbox GDK, PlayStation SDK, or similar)
  

Shift Timings: 9AM TO 6PM IST